Senior machine learning scientist leading predictive AI teams at TD, a major Canadian bank. Developing regression, classification, and anomaly detection models for personal banking.
About the role
- Machine Learning Engineer intern at Bree developing and deploying ML models for critical FinTech applications. Designing and architecting ML pipelines to enhance financial services.
Responsibilities
- Design, train, and deploy scalable machine learning models for critical FinTech applications, including credit risk assessment, fraud detection, and personalized financial recommendations, using frameworks like PyTorch and LightGBM.
- Architect ML pipelines integrating with backend systems to process high-throughput data streams with low-latency inference for real-time decision-making.
- Leverage AI tools to automate experimentation, hyperparameter tuning, and test-driven ML development, accelerating the delivery of robust, production-ready models.
- Support the full ML lifecycle, including feature engineering, model evaluation, A/B testing, monitoring for drift, and seamless scaling to support explosive user growth while ensuring compliance with financial regulations.
- Experiment with advanced techniques in deep learning and reinforcement learning to push the boundaries of what's possible in consumer finance.
Requirements
- Professional experience in building and deploying production ML systems and handling imbalanced datasets in high-stakes domains like finance or e-commerce.
- Good understanding of traditional ML systems and modern deep learning/reinforcement learning architectures, with a track record of applying them to real-world problems.
- Competitive ML experience (e.g., top rankings in Kaggle, NeurIPS challenges, or open-source contributions) is a bonus, demonstrating your ability to innovate under constraints and deliver high-performance models.
- Architectural thinking to solve ambiguous, data-driven problems in fast-paced settings, with experience scaling ML systems under explosive growth while maintaining accuracy, fairness, and explainability.
- Exceptional collaboration and communication skills, including the ability to explain complex ML concepts to non-technical stakeholders, thriving in low-churn teams focused on excellence, ethical AI, and long-term impact.
Benefits
- Compensation: $50-$65/hour, based on experience and interview performance
- Offer Matching: We're open to matching competing offers
- Perks: $250 monthly lunch stipend, bi-annual company retreat
- Impact: Push to prod, with 10x the ownership and impact of typical roles
- Growth: Mentorship programs and career training sessions
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